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- Creator:
- Dulka, Eden A
- Description:
- This data is a subset of that originally produced as part of an effort to characterize GnRH neuron activity during prepubertal development in control and PNA mice and investigate the potential influences of sex and PNA treatment on this process (1). It was later used in (2) to further investigate the firing patterns of GnRH neurons in these categories of mice and determine how these patterns might differ based on age and treatment condition. The data files can be opened and examined using Wavemetric's Igor Pro software. Code used to further examine and visualize the data can be found at https://gitlab.com/um-mip/mc-project-code. This research was supported by National Institute of Health/Eunice Kennedy Shriver National Institute of Child Health and Human Development R01 HD34860 and P50 HD28934. (1) Dulka EA, Moenter SM. Prepubertal development of gonadotropin-releasing hormone (GnRH) neuron activity is altered by sex, age and prenatal androgen exposure. Endocrinology 2017; 158:3941-3953 (2) Penix JJ, DeFazio RA, Dulka EA, Schnell S, Moenter SM. Firing patterns of gonadotropin-releasing hormone (GnRH) neurons are sculpted by their biology. Pending.
- Keyword:
- action potential, Monte Carlo, polycystic ovary syndrome, puberty, and androgen
- Citation to related publication:
- Dulka EA, Moenter SM. Prepubertal development of gonadotropin-releasing hormone neuron activity is altered by sex, age and prenatal androgen exposure. Endocrinology 2017; 158:3943-3953. https://dx.doi.org/10.1210%2Fen.2017-00768 and Penix JJ, DeFazio RA, Dulka EA, Schnell S, Moenter SM. Firing patterns of gonadotropin-releasing hormone (GnRH) neurons are sculpted by their biology. Pending.
- Discipline:
- Health Sciences
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- Creator:
- Vo, Thi and Glotzer, Sharon C.
- Description:
- The goal of this project is to develop a first principle driven approach for predicting the self-assembly behavior of entropically driven crystallization. We first developed a set of mean-field theoretical framework that captures the relevant energetic contributions to the assembly process and then evaluate relevant terms within our framework to determine the excess free energy of formation for each lattice (matlab/octave codes). We then validate theoretical predictions of relevant features like shape and bonding orbitals using standard MD simulations using HOOMD-Blue (simulation scripts). and This research was supported by the Office of the Undersecretary of Defense for Research and Engineering (OUSD(R&E)), Newton Award for Transformative Ideas during the COVID-19 Pandemic, Award number HQ00342010030.
- Keyword:
- Self-Assembly, Entropy, Thermodynamics, Simulations, and Theory
- Citation to related publication:
- Vo, T., & Glotzer, S. C. (2021). Microscopic Theory of Entropic Bonding for Colloidal Crystal Prediction. ArXiv:2107.02081 [Cond-Mat]. http://arxiv.org/abs/2107.02081
- Discipline:
- Science
-
- Creator:
- Hong, Yi, Fry, Lauren M., Orendorf, Sophie, Ward, Jamie L., Mroczka, Bryan, Wright, David, and Gronewold, Andrew
- Description:
- Accurate estimation of hydro-meteorological variables is essential for adaptive water management in the North American Laurentian Great Lakes. However, only a limited number of monthly datasets are available nowadays that encompass all components of net basin supply (NBS), such as over-lake precipitation (P), evaporation (E), and total runoff (R). To address this gap, we developed a daily hydro-meteorological dataset covering an extended period from 1979 to 2022 for each of the Great Lakes. The daily P and E were derived from six global gridded reanalysis climate datasets (GGRCD) that include both P and E estimates, and R was calculated from National Water Model (NWM) simulations. Ensemble mean values of the difference between P and E (P – E) and NBS were obtained by analyzing daily P, E, and R. Monthly averaged values derived from our new daily dataset were validated against existing monthly datasets. This daily hydro-meteorological dataset has the potential to serve as a validation resource for current data and analysis of individual NBS components. Additionally, it could offer a comprehensive depiction of weather and hydrological processes in the Great Lakes region, including the ability to record extreme events, facilitate enhanced seasonal analysis, and support hydrologic model development and calibration. The source code and data representation/analysis figures are also made available in the data repository.
- Keyword:
- Great Lakes, Hydrometeorological, National Water Model, Daily, Overlake precipitation, Overlake evaporation, Total runoff, Net Basin Supply, and Water Balance
- Discipline:
- Science and Engineering
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